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Cognitive Computation

Erschienen in:

01.03.2013

Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network

verfasst von: Huangqiong Chen, Zhigang Zeng

Erschienen in: Cognitive Computation | Ausgabe 1/2013

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Abstract

In this paper, a modified method for landslide prediction is presented. This method is based on the back-propagation neural network (BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning model and using the present data. This paper demonstrates that the revised BPNN modeling can be used to predict and calculate landslide deformation, quicken the learning speed of network, and improve the predicting precision. Applying this thinking and method into research of some landslide in the Three Gorges reservoir, the validity and practical value of this model can be demonstrated. And it also shows that the dynamic prediction of landslide deformation is very crucial.

Vorheriger Artikel Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions

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Titel: Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network
verfasst von: Huangqiong Chen
Zhigang Zeng
Publikationsdatum: 01.03.2013
Verlag: Springer-Verlag
Erschienen in: Cognitive Computation / Ausgabe 1/2013
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-012-9148-1

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